1. Field of the Invention
This invention relates to a computer implemented arrangement, which provides effective troubleshooting including fault diagnosis and which uses a hierarchy of rules with a learning knowledge base.
2. Description of the Prior Art
Expert systems, which are common in the art, are generally embodied in software for the purpose of solving difficult problems in some narrow domain of expertise. Typical examples of expert systems include troubleshooting expert systems, planning or scheduling expert systems and computer aided design expert systems. One example of a troubleshooting expert system is disclosed in the article by Y. Lirov, entitled "STAREX-simultaneous test and replace circuit pack troubleshooting expert system prototyping and implementation", Engineering Applications of Artificial Intelligence, Vol. 2, No. 1 (March 1989), pp. 3-18. That article discloses an expert system for troubleshooting including diagnosing faults in electronic circuit packs. Troubleshooting including diagnosing faults typically involves the existence of an error or a fault in some part or parts of a system and involves the use of some procedure to recognize or verify the fault and to correct the fault.
Troubleshooting expert systems such as the diagnostic expert system disclosed in the patent application of Y. Lirov and O. Yue, entitled "Technique for Producing an Expert System for System Fault Diagnosis", filed Jul. 28, 1989 as Ser. No. 386,325 typically include a plurality of modules like a knowledge based module and a sensor based module. The knowledge base module usually includes a set of rules that define conditions and conclusions, which are followed to solve diagnostic problems, while a typical sensor based module includes a human user, who is equipped with appropriate measurement instruments, called sensors. As an aside and to help understand the concept of rules, the following analog may be considered. The rules in a knowledge base can be compared to a node evaluation function in a typical problem of searching a graph to select the best node for expansion from a current list of candidate nodes whereby the best path to the correct system diagnosis can be found in the shortest amount of time using a minimum of user input. Continuing with our discussion, the rules in the knowledge based module produce directions to a human user in a sensor based module in such a manner that define characteristics such as the "what, when and where" parameters that are to be measured or replaced in the system being troubleshooted. The sensor based module, in turn, communicates the results of the measurements or replacements back to the knowledge base module. In that manner, an interactive selection of the most relevant rules can be accomplished, sometimes through the use of a third module, called an inference engine module. The actual communication in the interaction between the knowledge base module and the sensor based module is usually accomplished through a consultation module. Further details of these and other such modules may be obtained from any standard artificial intelligence textbook. See, for example, P. Winston, "Artificial Intelligence," Academic Press, (New York), 1984.
Unfortunately, it is common that people dislike receiving directions, or advice, for at least three reasons. One reason is that non-human systems tend to be stale unless some process exists to regularly update, replace, change, or alter the knowledge base module of the expert system. In other words it is usually desirable that the non-human system adapt to changes in much the same manner as a human would adapt to change. Another reason is that the time spent by the human in receiving the advice and communicating the results of the measurements back to the knowledge base module may be considered to be unproductive, i.e. may be viewed as wasted time. Still a third reason is that the non-human system may communicate indiscriminate advice instructing the user "what to do" without a satisfactory explanation to the human user as to the reason the user should follow the advice. The reluctance of human users to advice is particularly noticeable when the advice is communicated from a non-human system.
In our view, the above problems arise in the known art because it is an accepted procedure to bifurcate certain processes, e.g. it is common to firstly generate knowledge, or to acquire knowledge, and then to secondly utilize the acquired knowledge in an expert system as two separate and distinct, but repeated, processes. As the underlying process parameters change (e.g., some components in a system may become more likely to fail than others), the expert system may become less efficient and therefore it becomes necessary to repeat the knowledge generation process of the expert system.